A Compact Estimation of Distribution Algorithm for Solving Hybrid Flow-shop Scheduling Problem

According to the characteristics of the hybrid flow-shop scheduling problem (HFSP), the permutation based encoding and decoding schemes are designed and a probability model for describing the distribution of the solution space is built to propose a compact estimation of distribution algorithm (cEDA) in this paper. The algorithm uses only two individuals by sampling based on the probability model and updates the parameters of the probability model with the selected individual. The cEDA is efficient and easy to implement due to its low complexity and comparatively few parameters. Simulation results based on some widely-used instances and comparisons with some existing algorithms demonstrate the effectiveness and efficiency of the proposed compact estimation of distribution algorithm. The influence of the key parameter on the performance is investigated as well.